AI-Powered Cyberbullying Detection and Prevention for Safer Digital Spaces
By Soumiya Marichelvan, Aitizaz Ali, Uzair Ali
PDFAbstract
This research aims to address the growing issue of cyberbullying, particularly among youth, by developing an AI-powered system for real-time detection and prevention on digital platforms. The research focuses on understanding the various forms of cyberbullying and their detrimental effects on mental health. Using the Agile system development methodology, the research will follow an iterative process involving design, development, and testing phases to create a reliable detection tool. The system is intended to identify harmful online behaviour and alert parents or guardians promptly. Preliminary results suggest that the tool will feature an intuitive user interface and effective algorithms capable of detecting abusive interactions. This research is significant, as it proposes a technological solution to a pressing social issue, aiming to enhance the safety and well-being of young people online. Additionally, the research aligns with SDG 16 Peace, Justice, and Strong Institutions by contributing to the protection of children from online harm and promoting safer, more just digital environments.
Secure Multi-Party Computation for Cross-Institutional Query Federation without Raw Data Exposure in Healthcare Databases
By Haider H. Razzaq
PDFAbstract
Cross-institutional healthcare research requires secure analysis of patient data distributed across hospitals while complying with the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). Existing federated query platforms restrict analyses to predefined aggregates and may expose institution-level statistics, whereas federated learning remains vulnerable to information leakage. This paper proposes a Secure Multi-Party Computation (SMPC) framework for privacy-preserving federated Structured Query Language (SQL) queries across multiple healthcare institutions without disclosing individual records or intermediate results. The framework introduces a federated query language and compiler that transforms queries into depth-bounded arithmetic circuits. It integrates verifiable secret sharing, Beaver-triple generation, and distributed key generation to support secure equality testing, comparison, record linkage, and aggregation. Protocol complexity and security guarantees are analyzed under semi-honest and malicious adversarial models, with malicious security approximately doubling communication requirements. An analytical cost model, calibrated using the SPDZ family of Multi-Party Computation (MPC) frameworks, evaluates scalability across 3–30 institutions and 1,000–500,000 patient records per institution. Results project that a representative cross-institutional linkage query requires less than one second and approximately 77 megabytes of communication for 15 institutions containing 10,000 records each. Four healthcare case studies demonstrate the framework’s potential applicability to rare-disease cohort discovery, drug-safety surveillance, and comparative-effectiveness research. The proposed framework provides a scalable approach to privacy-preserving healthcare analytics while supporting regulatory compliance and enabling secure collaborative research without centralizing sensitive patient information.
Solar-Powered Wireless Mesh Streetlight Monitoring System for Smart Cities
By Mahmood A. Al-Shareeda, Ahmed Thabet Abdul-Wahid, Atheer Hani Dinar, Hussein Mohammed Mustafa, Wael Salman Jabbar
PDFAbstract
Urban road lighting is a large-scale municipal controllable energy source with enormous room for optimization. At present, most already deployed street lamps still operate on fixed schedules, maintaining uniform full brightness across all time periods regardless of actual lighting demand. The solar-powered smart street lamp platform developed in this project is adapted for smart city scenarios, integrates core functional modules including renewable energy collection and adaptive dimming, and is equipped with an ESP32 controller, light sensors, and motion sensing hardware. It enables intelligent operation, comprehensively improving the efficiency, safety, and maintain-ability of street lamps. The core objective of the solar-powered connected smart streetlight system designed in this study is to leverage a wireless network architecture and management control dashboard to achieve synchronized reporting of the status, power level, and charging data of large-scale streetlight deployments, as well as fault alarms, to support remote management and control for operation and maintenance personnel. We built a prototype and completed three tests: adaptive brightness control reduced energy consumption from 288Wh to 108Wh, marking a 62.5% energy saving; motion-triggered lighting registered an average response time of 180ms and a detection success rate of 97.9%; the Blynk-based remote monitoring function recorded an average cloud latency of 245ms and a connection availability rate of 99.7%. This system can substantially cut energy consumption and improve the visibility and response efficiency of operation and maintenance efforts for smart cities.
Blockchain-Enabled Zero Trust Architecture for Securing Cloud Computing based on Cybersecurity Risk Auditing Framework
By Mohammed Almaiah, Santosh Reddy Addula
PDFAbstract
Traditional cloud security controls tend to treat access, vulnerability exposure, and audit integrity as distinct functions, reducing the ability to be aware of risks on an ongoing basis. In this study, the authors present a blockchain-based zero-trust risk auditing framework for adaptive cloud protection (ACP) called BAZTRA. BAZTRA is a combination of a GBDT-based network-risk estimator and identity, device-posture, provenance, vulnerability, and compliance evidence. The CICIoT2023 corpus, controlled contextual replay, and repeated ten-seed experiments were used to evaluate the following techniques: availability-aware fusion, temporal smoothing, four-state enforcement, EPSS-KEV prioritization, hash chaining, and Merkle-based audit anchoring. Statistical significance was assessed using Friedman and Holm-corrected Wilcoxon tests across repetitions. The selected network-risk estimator achieved 78.96% accuracy, 55.01% macro-F1, 0.964 ROC-AUC, and 0.718 MCC. The complete BAZTRA framework produced 89.44% decision macro-F1, reduced unsafe access to 0.67%, and limited false denial to 0.03%. Ablation confirmed that adaptive enforcement, network evidence, provenance analysis, and availability-aware normalization were the most influential components. Merkle batching reduced effective ledger payload by 98.21% while preserving detection of modification, deletion, reordering, and replay attempts. BAZTRA provides a unified approach to continuous access assessment, vulnerability prioritization, and accountable cloud auditing. Future work will validate the framework on complete multi-source telemetry and a physical multi-peer Hyperledger Fabric deployment.